Specific Topology Research Articles

Single-Chain Nanoparticles under Homogeneous Shear Flow

Single-chain nanoparticles (SCNPs) are a new class of macromolecular objects, synthesized through purely intramolecular cross-linking of single polymer chains. We use a multi-scale hydrodynamics simulation approach to study, for the first time, SCNPs under shear flow. We investigate the case of irreversible SCNPs (permanent cross-links) in dilute solution. SCNPs emerge as a novel class of macromolecular objects with response to shear distinct from other systems such as linear chains, star polymers, rings or dendrimers. This is evidenced by the observed set of scaling exponents for the shear rate dependence of the SCNP static and dynamic properties. Surprisingly these exponents are, at most, marginally dependent on the specific topology of the SCNP (globular or sparse), suggesting that they are inherently related to the network-like character of the molecular architecture and not to its specific connectivity. At high Weissenberg numbers the dynamics of the sparse SCNPs is dominated by tumbling motion, whereas tank-treading predominates for the most globular SCNPs.

ALCHEMY IN THE CONTEMPORARY WORLD – MAGNUM IGNOTUM

Alchemy in modern consciousness ceased to be perceived as a scientific fallacy which led to a mass of fruitless attempts to obtain the philosopher's stone, that is, some substance through which one can turn lead into gold. In modern artistic, philosophical and psychological works alchemy is present as a phenomenon the essence of which is mysterious and incomprehensible, or is lost. Alchemy and the philosopher's stone today in society are perceived as Magnum ign?tum (the Great Unknown). This perception is due, among other things, to the diversity of approaches to studying alchemy and its phenomena. The purpose of the article is to present the diversity of historically developed approaches to studying alchemy. The article attempts to apply the method of phenomenology of religion to alchemy. Phenomenology emphasizes, first of all, a structural relationship, rather than a historical one. The phenomenological method of investigation finds concretization in the geometric representation of the structure of alchemy, which correlates with the structure of the investigation of this phenomenon. The article shows that the diversity of approaches reflects the special topology of the phenomenon of alchemy, which makes it possible to obtain its various geometric sections and consider them separately, yet none of them embraces the phenomenon of alchemy as a whole. The materials of the article can be useful for a historical understanding of the motives for the development of science.

A Secondary Fan-spine Magnetic Structure in Active Region 11897

Abstract Fan-spine is a special topology in solar atmosphere and is closely related to magnetic null point, as well as circular-ribbon flares, which can provide important information for understanding the intrinsic 3D nature of solar flares. However, the fine structure within the fan has rarely been investigated. In present paper, we investigate a secondary fan-spine (SFS) structure within the fan of a larger fan-spine topology. On 2013 November 18, this large fan-spine structure was traced out owing to the partial eruption of a filament, which caused a circular-ribbon flare in NOAA Active Region 11897. The extrapolated 3D magnetic fields and squashing factor Q maps depict distinctly this fan-spine topology, its surrounding quasi-separatrix layer (QSL) halo, and a smaller quasi-circular ribbon with high Q located in the center, which implies the existence of fine structure within the fan. The imaging observations, extrapolated 3D fields, and Q maps on November 17 show that there indeed exists an SFS surrounded by a QSL, which is enveloped by another QSL halo corresponding to the overlying larger dome-shaped fan. Moreover, the material flows caused by the null-point reconnection are also detected along this SFS. After checking the evolution of the underneath magnetic fields, we suggest that the continuous emergence of magnetic flux within the central parasitic region encompassed by the opposite-polarity fields results in the formation of the SFS under the large fan.

Topological Analysis of Transthyretin Disassembly Mechanism: Surface-Induced Dissociation Reveals Hidden Reaction Pathways.

The proposed mechanism of fibril formation of transthyretin (TTR) involves self-assembly of partially unfolded monomers. However, the mechanism(s) of disassembly to monomer and potential intermediates involved in this process are not fully understood. In this study, native mass spectrometry and surface-induced dissociation (SID) are used to investigate the TTR disassembly mechanism(s) and the effects of temperature and ionic strength on the kinetics of TTR complex formation. Results from the SID of hybrid tetramers formed during subunit exchange provide strong evidence for a two-step mechanism whereby the tetramer dissociates to dimers that then dissociate to monomers. Also, the SID results uncovered a hidden pathway in which a specific topology of the hybrid tetramer is directly produced by assembly of dimers in the early steps of TTR disassembly. Implementation of SID to dissect protein topology during subunit exchange provides unique opportunities to gain unparalleled insight into disassembly pathways.

On Basic Boolean Function Graphene Nanoribbon Conductance Mapping

In this paper, we augment a trapezoidal Quantum Point Contact topology with top gates to form a butterfly Graphene Nanoribbon (GNR) structure and demonstrate that by adjusting its topology, its conductance map can mirror basic Boolean functions, thus one can use such structures instead of transistors to build carbon-based gates and circuits. We first identify by means of Design Space Exploration specific GNR topologies for 2- and 3-input {AND, NAND, OR, NOR, XOR, XNOR} and demonstrate by means of the Non-Equilibrium Green Function - Landauer based simulations that butterfly GNR-based structures operating at $V_{\text {DD}}=$ 0.2 V outperform 7 nm @ $V_{\text {DD}}=$ 0.7 V CMOS counterparts by 2 to 3, 1 to 2, and 3 to 4, orders of magnitude in terms of delay, power consumption, and power-delay product, respectively, while requiring 2 orders of magnitude less active area. Subsequently, we investigate the effect of $V_{{\text {DD}}}$ variations and the $V_{{\text {DD}}}$ value lower bound. We demonstrate that the NOR butterfly GNR structures are quite robust as their conductance and delay are changing by no more than 2% and 6%, respectively, and that AND and NOR GNR geometries can operate even at 10 mV. Finally, we consider the aspects related to the practical realization of the proposed structures and conclude that even if there are still hurdles on the road ahead the latest graphene fabrication technology developments, e.g., surface-assisted synthesis, our proposal opens an alternative towards effective carbon-based nanoelectronic circuits and applications.

Distributed Platoon Control Under Topologies With Complex Eigenvalues: Stability Analysis and Controller Synthesis

The platooning of autonomous vehicles can significantly benefit road traffic. Most previous studies on platoon control have only focused on specific communication topologies, especially those with real eigenvalues. This paper extends existing studies on distributed platoon control to more generic topologies with complex eigenvalues, including both internal stability analysis and linear controller synthesis. Linear platoon dynamics are derived using an inverse vehicle model compensation, and graph theory is employed to model the communication topology, leading to an integrated high-dimension linear model of the closed-loop platoon dynamics. Using the similarity transformation, a sufficient and necessary condition is derived for the internal stability, which is completely defined in real number field. Then, we propose a Riccati inequality based algorithm to calculate the feasible static control gain. Further, disturbance propagation is formulated as an $\text {H}_{\infty }$ performance, and the upper bound of spacing errors is explicitly derived using Lyapunov analysis. Numerical simulations with a nonlinear vehicle model validate the effectiveness of the proposed methods.

An Efficient Planning Algorithm for Hybrid Remote Microgrids

This paper presents an efficient planning algorithm for microgrids in remote isolated communities. Different from the existing research that assumes a specific microgrid topology, we propose a planning algorithm that jointly specifies the optimal grid topology, namely AC, DC, or hybrid AC/DC, along with the optimal locations and sizes of distributed energy resources, energy storage systems, and AC–DC converters. The planning objective is to ensure reliable power flow with minimum deployment and operational costs. The planning problem is formulated as a mixed integer nonlinear program, and given the complexity of the problem, the proposed algorithm implements a two-stage framework that results in an efficient planning solution. The first stage deals with the specification of the microgrid topology, and allocation and sizing of all the equipment following a heuristic optimization approach. Upon deciding the microgrid topology and equipment installation in the first stage, the second stage of the planning algorithm ensures smooth and reliable operation for the proposed topology over all possible operation scenarios. This is achieved with minimal operational costs by considering the optimal nonlinear scheduling problem for the installed equipment. Test cases are presented to investigate the performance of the proposed planning algorithm at different fuel transportation cost scenarios.

Fully Convolutional Boundary Regression for Retina OCT Segmentation.

A major goal of analyzing retinal optical coherence tomography (OCT) images is retinal layer segmentation. Accurate automated algorithms for segmenting smooth continuous layer surfaces, with correct hierarchy (topology) are desired for monitoring disease progression. State-of-the-art methods use a trained classifier to label each pixel into background, layer, or surface pixels. The final step of extracting the desired smooth surfaces with correct topology are mostly performed by graph methods (e.g. shortest path, graph cut). However, manually building a graph with varying constraints by retinal region and pathology and solving the minimization with specialized algorithms will degrade the flexibility and time efficiency of the whole framework. In this paper, we directly model the distribution of surface positions using a deep network with a fully differentiable soft argmax to obtain smooth, continuous surfaces in a single feed forward operation. A special topology module is used in the deep network both in the training and testing stages to guarantee the surface topology. An extra deep network output branch is also used for predicting lesion and layers in a pixel-wise labeling scheme. The proposed method was evaluated on two publicly available data sets of healthy controls, subjects with multiple sclerosis, and diabetic macular edema; it achieves state-of-the art sub-pixel results.

A Novel Systolic Parallel Hardware Architecture for the FPGA Acceleration of Feedforward Neural Networks

New chips for machine learning applications appear, they are tuned for a specific topology, being efficient by using highly parallel designs at the cost of high power or large complex devices. However, the computational demands of deep neural networks require flexible and efficient hardware architectures able to fit different applications, neural network types, number of inputs, outputs, layers, and units in each layer, making the migration from software to hardware easy. This paper describes novel hardware implementing any feedforward neural network (FFNN): multilayer perceptron, autoencoder, and logistic regression. The architecture admits an arbitrary input and output number, units in layers, and a number of layers. The hardware combines matrix algebra concepts with serial-parallel computation. It is based on a systolic ring of neural processing elements (NPE), only requiring as many NPEs as neuron units in the largest layer, no matter the number of layers. The use of resources grows linearly with the number of NPEs. This versatile architecture serves as an accelerator in real-time applications and its size does not affect the system clock frequency. Unlike most approaches, a single activation function block (AFB) for the whole FFNN is required. Performance, resource usage, and accuracy for several network topologies and activation functions are evaluated. The architecture reaches 550 MHz clock speed in a Virtex7 FPGA. The proposed implementation uses 18-bit fixed point achieving similar classification performance to a floating point approach. A reduced weight bit size does not affect the accuracy, allowing more weights in the same memory. Different FFNN for Iris and MNIST datasets were evaluated and, for a real-time application of abnormal cardiac detection, a ${\hspace{-0.112em}\times \hspace{-0.112em}}256$ acceleration was achieved. The proposed architecture can perform up to 1980 Giga operations per second (GOPS), implementing the multilayer FFNN of up to 3600 neurons per layer in a single chip. The architecture can be extended to bigger capacity devices or multi-chip by the simple NPE ring extension.

USING EXCESS CODE TO DESIGN FAULT-TOLERANT TOPOLOGIES

and in modernizing an already existing one. Particular attention is paid to it when building multicomputer systems or clusters. The most interesting ways to increase fault tolerance is to use the topological structure of the system to bypass the malfunction and use one or another element of the system to replace the faulty. Of course, this requires the development of a specific topology. The article deals with the development of fault-tolerant versions of popular topologies, such as quasiquantum and hypercube, based on the excess code 0/1 /-1. Target setting. An important part of any multicomputer system or cluster is its topological structure. This structure defines the routing of messages in the system, speed of message transmission, and level of fault-tolerance of a system. The article proposes a method for increasing fault-tolerance based on the use of excess code. Actual scientific researches and issues analysis. Synthesis of topologies such as the hypercube or the de Bruin topology is well studied and described now, there are papers consider methods for increasing the fault-tolerance that based on usage of additional nodes that duplicate current nodes. Other papers consider using a tree-based routing to improve fault-tolerance of the system. Uninvestigated parts of general matters defining. Now the possibilities of using excess code 0/1/-1 for creating new fault-tolerant topologies based on existing synthesis methods are unconsidered. The research objective. The task is to describe the synthesis of fault tolerant topologies, the consideration of the possibilities of using their features and the analysis of the main characteristics in comparison with each other and with classic versions based on binary code. The statement of basic materials. The synthesis of hypercube and de Bruin topology is described on the basis of the usual binary code and the redundant code 0/1 / -1, the possibilities of using redundancy are considered, first of all, to increase the fault-tolerance, a comparative analysis of all of these topologies is carried out. Conclusions. The analysis of characteristics is performed, the main advantages and disadvantages of the proposed topological structures are highlighted, suggestions for their improvement are made.

Multiple-input bulk-driven quasi-floating-gate MOS transistor for low-voltage low-power integrated circuits

This brief presents the first experimental results of the multiple-input bulk-driven quasi-floating-gate (MI-BD-QFG) MOS transistor (MOST) which is suitable for low-voltage (LV) low-power (LP) integrated circuits design. The MI-BD-QFG MOST is an extension to the principle of the bulk-driven quasi-floating-gate (BD-QFG) MOST. However, unlike the BD-QFG the MI-BD-QFG MOST offers multiple-input that simplifies specific CMOS topologies and reduce their power consumption. To confirm the advantages of the MI-BD-QFG MOST a Differential Difference Current Conveyor (DDCC) with very simple CMOS structure has been designed and fabricated in a standard n-well 0.18 µm CMOS process from TSMC with total chip area 350 µm × 78 µm. The fabricated circuit uses a 0.5 V power supply, consumes 1.7 µW power and offers near rail-to-rail input common mode range.

Controllability of Two-Time-Scale Discrete-Time Multiagent Systems.

In this paper, the controllability problem is addressed for a two-time-scale discrete-time system with multiple agents. First, the system is described by a singularly perturbed difference equation expressed on fast timescale. Then, to eliminate the singular perturbation parameter, by using the iterative method and approximate approach, the two-time-scale system is separated into slow and fast subsystems. Subsequently, some sufficient and/or necessary conditions of controllability for the systems are derived by using matrix theory. Moreover, under three special network topologies, the necessary criteria for controllability are proposed via graph theory. Finally, we give a simulation example to illustrate the effectiveness of the proposed theoretical results.

New Methodology for Evaluation of Non-pilot Relay Distance Protection

Distance relays are typically used in transmission line protection. Their accuracy depends on the correct relay parameterization, phasor estimators, and correct usage of distance characteristics. Identifying suitable relay parameterization and algorithms considering multiple transmission line with various topologies and different fault types is a hard task. Here, a methodology based on the relay trip performance is proposed to evaluate all the main concerns of distance protection such as: maloperation trips per relay units in each fault type, overreach operation, and maloperation due to faults closer to the relay. The methodology could identify the best phasor estimators and distance configurations among those evaluated, as well as it could verify the power system topologies which yield in challenges for distance protection. The results achieved by the proposed methodology demonstrated that it can be useful for assisting the development of phasor estimators and new distance characteristics, as well as for setting existing distance protection in specific power system topologies.

RETRACTED ARTICLE: The construction and simulation of internet financial product diffusion model based on complex network and consumer decision-making mechanism

Nowadays, Internet financial products, like Internet banking service and e-payment, have permeated into every corner of civil life in China. Via the Internet and media, consumers can obtain big data and information of financial products. Consumer big data analytics capabilities directly affect consumers’ cognition and thus affect their adoption of Internet financial products. Upon this background, this paper aims to study the diffusion of Internet financial products in a big data environment based on consumer analytics capabilities. First, in light of the consumer decision-making mechanism, we build a random threshold model for the diffusion of Internet financial products. Next, we explore the impact of consumer big data analysis capabilities on the diffusion of Internet financial products under different proportion of initial adopter and network densities in a specific social network topology. The simulation result shows that the consumer big data analytics capabilities have a significant impact on the speed and depth of the diffusion of Internet financial products. However, when proportion of initial adopter and network density reaches a certain value, the impact of consumer big data analysis ability on diffusion speed and depth has decreased. This study has a guiding significance for new strategies of Internet financial products promotion.

Influencers identification in complex networks through reaction-diffusion dynamics

A pivotal idea in network science, marketing research and innovation diffusion theories is that a small group of nodes -- called influencers -- have the largest impact on social contagion and epidemic processes in networks. Despite the long-standing interest in the influencers identification problem in socio-economic and biological networks, there is not yet agreement on which is the best identification strategy. State-of-the-art strategies are typically based either on heuristic centrality metrics or on analytic arguments that only hold for specific network topologies or peculiar dynamical regimes. Here, we leverage the recently introduced random-walk effective distance -- a topological metric that estimates almost perfectly the arrival time of diffusive spreading processes on networks -- to introduce a new centrality metric which quantifies how close a node is to the other nodes. We show that the new centrality metric significantly outperforms state-of-the-art metrics in detecting the influencers for global contagion processes. Our findings reveal the essential role of the network effective distance for the influencers identification and lead us closer to the optimal solution of the problem.

A priori bond-valence and bond-length calculations in rock-forming minerals

Within the framework of the bond-valence model, one may write equations describing the valence-sum rule and the loop rule in terms of the constituent bond valences. These are collectively called the network equations, and can be solved for a specific bond topology to calculate its a priori bond valences. A priori bond valences are the ideal values of bond strengths intrinsic to a given bond topology that depend strictly on the formal valences of the ion at each site in the structure, and the bond-topological characteristics of the structure (i.e. the ion connectivity). The a priori bond valences are calculated for selected rock-forming minerals, beginning with a simple example (magnesiochromite, = 1.379 bits per atom) and progressing through a series of gradually more complex minerals (grossular, diopside, forsterite, fluoro-phlogopite, phlogopite, fluoro-tremolite, tremolite, albite) to finish with epidote (= 4.187 bits per atom). The effects of weak bonds (hydrogen bonds, long Na+—O2− bonds) on the calculation of a priori bond valences and bond lengths are examined. For the selected set of minerals, a priori and observed bond valences and bond lengths scatter closely about the 1:1 line with an average deviation of 0.04 v.u. and 0.048 Å and maximum deviations of 0.16 v.u. and 0.620 Å. The scatter of the corresponding a priori and observed bond lengths is strongly a function of the Lewis acidity of the constituent cation. For cations of high Lewis acidity, the range of differences between the a priori and observed bond lengths is small, whereas for cations of low Lewis acidity, the range of differences between the a priori and observed bond lengths is large. These calculations allow assessment of the strain in a crystal structure and provide a way to examine the effect of bond topology on variation in observed bond lengths for the same ion-pair in different bond topologies.

A Networked <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math> </inline-formula>-Player Trust Game and Its Evolutionary Dynamics

Trust and trustworthiness are of great importance in social and human systems, especially when considering managerial and economic decision-making. In this paper, we investigate the emergent dynamics of an evolutionary game-theoretic model—the ${N}$ -player evolutionary trust game—consisting of three types of players: 1) an investor; 2) a trustee who is trustworthy; and 3) a trustee who is untrustworthy. Here, we limit the interactions between players to local neighborhoods defined by a specific spatial topology or social network. Players are able to adjust their game-playing strategies using an evolutionary update rule based on the payoffs obtained by their neighbors. Through comprehensive simulation experiments, we find that trust can be promoted when players interact on a social network despite a substantial number of untrustworthy individuals in the initial population. These results differ from findings reported for an unstructured population of the same game, where the existence of a single untrustworthy individual would eliminate trust completely. We compare the dynamics of the model with different social network densities and structures (e.g., from regular lattices to scale-free and random networks). We observe that the levels of trust vary under different network structures, and the level is correlated with how “difficult” the game is. When game conditions are easy (i.e., low temptation to defect and/or almost no initial untrustworthy trustees), homogeneous networks with higher densities can promote higher levels of trust. However, when the game becomes harder, heterogeneous social networks with lower densities are able to promote higher levels of trust and global net wealth.

Dual Quasi-Halbach Linear Tubular Actuator With Coreless Moving-Coil for Semiactive and Active Suspension

This paper proposes the application of a specific topology of coreless linear tubular actuator for semiactive and active suspension systems. The actuator consists of two quasi-Halbach arrays of permanent magnets, inner and outer, and a moving-coil armature. Some of the advantages of this actuator for the proposed applications include low moving mass, low ripple of force, reduced magnetic losses, and high force density. The paper also introduces a new method for determining actuator requirements to operate with an active and/or semiactive suspension system and presents the application of the proposed approach to a harmonically base excited single degree-of-freedom suspension system. Additionally, the electromagnetic model for magnetic field distribution, open-circuit induced voltage, and axial force and force ripple was developed and validated by means of finite-element analysis and with experimental results obtained from a prototype.

Understanding the enhanced synchronization of delay-coupled networks with fluctuating topology

We study the dynamics of networks with coupling delay, from which the connectivity changes over time. The synchronization properties are shown to depend on the interplay of three time scales: the internal time scale of the dynamics, the coupling delay along the network links and time scale at which the topology changes. Concentrating on a linearized model, we develop an analytical theory for the stability of a synchronized solution. In two limit cases, the system can be reduced to an “effective” topology: in the fast switching approximation, when the network fluctuations are much faster than the internal time scale and the coupling delay, the effective network topology is the arithmetic mean over the different topologies. In the slow network limit, when the network fluctuation time scale is equal to the coupling delay, the effective adjacency matrix is the geometric mean over the adjacency matrices of the different topologies. In the intermediate regime, the system shows a sensitive dependence on the ratio of time scales, and on the specific topologies, reproduced as well by numerical simulations. Our results are shown to describe the synchronization properties of fluctuating networks of delay-coupled chaotic maps.

An autonomous optimal control strategy for reducing Gaussian laser beam constraint effect on photovoltaic array

A novel autonomous optimal control strategy on a photovoltaic array under a Gaussian beam constraint condition for a high-intensity laser power beaming system is proposed in this paper. The proposed strategy divides the PV array into several concentric parallel branches according to Gaussian irradiance distribution and optimizes the series and parallel forms of each concentric branch and its PV cells, which is not dependent on the specific PV array topology and can obtain an optimal connection form matching with the system under Gaussian beam constraint condition. The proposed strategy can be applied to the different connection forms of PV array for minimizing the Gaussian constraint effect by utilizing the characteristics of Gaussian irradiance distribution on laser facula and the inherent error values in the matching area of the laser facula and the PV array, and the output power and the efficiency of PV array can reach an optimal value simultaneously. The uneven irradiance of Gaussian laser facula is mathematically modeled in this paper. The correctness of theoretical analysis is verified by simulation results.